Walking the path towards the Smart Finance Function
The 6 levels of automation in the Finance Function based on the autonomous driving standard J3016.
Researches point out that up to 80% of tasks can be automated in certain areas of the Finance Function. Finance teams are under pressure to provide more insights to support business decisions and other high value strategic activities. A move to speed up standardization and automation is seen now by CFOs as the best way to free up the time needed to keep Finance teams focused on high-value activities.
The journey to pursue excellence in finance operations has to start with an introspective exercise of searching the possibilities which provides the latest platforms and their combination with new technologies. Artificial Intelligence capabilities such as Machine Learning, RPA (Robotic Process Automation), OCR (Optical Character Recognition) or NLP (Natural Language Processing) can now be part of the tech resources when it comes to process automation since the complexity of adding this type of intelligent features is being significantly reduced.
In the short term the adoption of these new technologies will take off and unleash more advanced business models. This will make business areas rethink their operations in place, not only in the Finance Function, but the rest of production and service departments. In addition, Project Management best practices, especially agile methodologies, will aid to get into the path of innovation whilst maximizing the return of the investment.
CFOs in collaboration with CEOs, should promote this innovative thinking across Finance to unfold new ways of working that let companies be ready for tomorrow´s challenges.
Looking at the automotive sector in the search of similarities to elaborate a manageable set of levels of standardization and automation for Finance Functions, in 2014 The Society of Automotive Engineers (SAE) issued the standard J3016 which defined a common understanding of the different grades of Autonomous Driving for the automotive stakeholders. Forecasts point out that by 2025, 8 million autonomous cars will be present in our roads, therefore the referred standard aims to prepare the way towards the massive adoption of Automated Drive Systems (ADS).
In the same way, this document aims to introduce the statement of the automation scale applicable for the Finance Function and its stakeholders. Its goal is to be an awareness tool to rate the level of automation through a straightforward white paper to ease the evolution of Financial models towards the most advanced stage of the proposed scale, denominated as Smart Finance Function.
Introduction to the levels of automated driving (standard J3016)
Before getting introduced to the scale of Finance Function´s automation, let´s first have a look at the
autonomous standard J3016, which was the foundations of our statement.
In January 2014, The Society of Automotive Engineers (SAE) issued a classification system for self-driving
cars. The categorization provided a common frame of understanding including taxonomies and definitions
that were adopted by the US National Traffic Safety Administration and Department of Transportation,
moreover they were also adopted as a standard by the global car industry´s stakeholders. The standard has
been evolving during the past years, introducing more clear elements and definitions for the car
manufacturers and other bodies to speed up the regulatory framework and best practices in the design,
testing and deployment of highly automated vehicles (HAVS).
Levels go from 0 (fully manual) to 5 (fully autonomous) depending on the driver assistance advancements.
“Level 0 - No automation
At Level 0, the driver performs all operating tasks like steering, braking, accelerating or slowing down,
although there may be systems in place to help the driver as the emergency braking system.
Level 1 - Driver assistance
At this level, the vehicle can assist with some functions, but the driver still handles all accelerating, braking,
and monitoring of the surrounding environment. For instance, adaptive cruise control helps the driver to
keep a security distance behind the next car.
Level 2 - Partial automation
Most automakers are currently developing vehicles at this level, where the vehicle can assist with steering or acceleration functions and allow the driver to disengage from some of their tasks. The driver must always be ready to take control of the vehicle and it is still responsible for most safety-critical functions and all monitoring of the environment.
Level 3 - Conditional automation
The vehicle itself controls all monitoring of the environment using sensors such as LiDAR. The driver’s
attention is still critical at this level but can disengage from “safety-critical” functions like braking and leave it to the technology when conditions are safe.
Level 4 - High automation
Level 4 vehicles can intervene if things go wrong or there is a system failure. In this sense, these cars do not
require human interaction in most circumstances. However, a human still has the option to manually
override. Level 4 vehicles can operate in self-driving mode. But until legislation and infrastructure evolves,
they can only do so within a limited area and speed.
Level 5 - Full automation
This level of autonomous driving requires absolutely no human attention. There is no need for pedals,
brakes, or a steering wheel, as the autonomous vehicle system controls all critical tasks, monitoring of the
environment and identification of unique driving conditions like traffic jams.”
Check this out for further details of SAE´s Autonomous driving standard and an additional reference used in this section.
Levels of maturity in the Finance Function
At The Future of Finance Function we have developed a scale to assess the level of maturity of the
finance operations to provide guidance and support to areas such as Financial Accounting, Controlling,
Treasury, Internal and External reporting, Tax, as well as others related like Internal Audit, Corporate Social
Responsibility, Legal, etc., on their way to the future of finance.
This statement aims to facilitate a common frame of understanding within a predefined range of maturity
levels for Finance Function professionals and their stakeholders based on a specific usage of technologies
and grades of automation supporting each level.
Similarly to J3016 standard, the proposed scale goes from level 0 (fully manual) to level 5 (fully automated)
depending on the level of system´s assistance in the financial operations.
Level 0 - No automated procedures
At this level, finance departments execute most of their operations manually, like invoice processing,
account payables or receivables reconciliations, tax reporting, elaboration of financial statements, etc. The
data can be either on different paper-based documents or partially integrated in spreadsheets, which
makes traceability and reconciliation tedious tasks. Lack of standardization prevails at this stage. This level
would be comprehensible only in companies of recent creation or for small ones where reduced volumes of information might be managed easily.
Level 1 - Basic assistance
In level 1, most of core processes (Sales, Logistics, Accounting, HR, etc.) are covered through ERP
systems with extended reporting capabilities supported by Analytics tools, but still a high range of tasks
remains manual or semi-automated. Basic workflows are in place for some processes and automatic
operations are present but with a low ability of escalation in case of new regulations or business
requirements. Multiple sources and lack of data homogenization require manual processing for reporting
which increases the probability of data inaccuracies. The lack of a common data repository affects
negatively to internal and external reporting processes generating manual reconciliation activities.
Level 2 - Partial automation
Level 2 brings the introduction of RPA, OCR and NLP tools at basic level for straightforward and local
processes to gain efficiencies either at individual or team level. Level 2 also adds improvements as a
common source of information established for both internal and external reporting which reduces
remarkably reconciliation processes. This level lets IT to disengage from some tasks such as master data
maintenance since replication across systems is handled via Master Data Management tools or fully
integrated systems. Controls are essential at this level and business models should point out sharply output
inconsistencies for tightening response times. Centers of excellence (centralized, decentralized or mixed)
are in place to articulate support and improvements of business models.
Level 3 - Half way towards Automation Excellence
This category brings the introduction of Supervised Learning models and extended RPA usage which
provides an important leverage to streamline operations with a manual task reduction up to 40%. ML
techniques such as regression, classification or decision trees can introduce relevant improvements in the
finance operations and their linked IT support. These algorithms rely mostly on historical and labeled data
which requires an intensive preparation phase to assure the quality of inputs and a plan to retrain the ML
models periodically. The risk of having work overload during closing periods is notably reduced.
Level 4 - High automation
At this stage, most manual activities are handled through RPA with a partial or full integration with finance
models. Some operations do not require manual intervention, however teams still have the option to
manual override if needed. Therefore teams are more focused on supervising automatic outputs rather than
executing operations manually. AI skills start to be usual in part of the finance workforce. Unsupervised
learning can unchain new use cases to expand analysis capabilities to elaborate more valuable insights to
the business. Up to 60% of the time dedicated to manual and repetitive tasks is freed up.
Level 5 - Smart Finance Function
Level 5 adds up intensive usage of AI capabilities and the introduction of advanced Machine Learning
techniques such as Reinforcement Learning or combinations of different ML techniques already presented
in previous levels. These models are completely integrated with the core financial systems to move the
finance operations to the next level. No human intervention can be achieved in many processes which in
some cases will lead to auto-pilot business models. Leading organizations might achieve a new way of
handling finance closings almost without any manual activities and with real-time access to the information
to boost the process of decision making.
Why Finance Operations should be headed toward the model of
Smart Finance Function
The Euro NCAP (European New Car Assessment Programme), which rates the passenger safety in vehicles,
raises every 2 or 3 years the levels of their tests to be eligible to the best security performance category, 5
stars. Thus, a car rating 5 stars in a specific year can be downgraded to 3 stars right after the release of a
new rating criteria. This represents a good analogy of why improvements introduced in Finance Operations
formerly have to be continuous targets for improvement as result of the constant evolution of technologies.
Companies need to embrace a continuous improvement mindset to lead their way toward the Smart
Finance Function, and results will also bring positive impacts in other essential KPIs such as customer and
employee experiences.
Same as LIDAR (Light Detection and Ranging) technology which is presumedly the base to build confident
autonomous driving capabilities, evolution of AI technologies such as RPA, Machine Learning, NLP and
OCR are now capable of unleashing both efficiency and effectiveness in Finance Operations with a fast and
cost effective manner during the coming years.
Focus just in technology would be a mistake as deep changes should get acceptance within organizations
at all levels, including heavy support of the top management. This makes organizational cultural
evolution and governance fundamental during the initial thoughts before reimagining the company's
operational processes.
Recent unexpected changes as the quick introduction of remote work were quickly adopted by agile
organizations just in a few days for keeping social distancing measures as a contingency measure against
the COVID-19, so this type of agility mindset must be encouraged to redefine the company´s strategic
plans.
Project Managers and PMOs have a key role across this journey which is to get the most out of each euro
invested in digital transformations. They act as facilitators building a safe road for cross-functional teams
when it's time for innovation in changing environments.
Car manufacturers are concentrating their efforts on customer´s most valued features, autonomous driving,
electrification and connected cars. Similarly, CFOs are aware that non-value added activities in finance
operations should be standardized and automated constantly to allocate time where it matters most now,
strategic initiatives and valuable insights to the business areas to generate competitive advantages.
During the last 10 years, most of the Finance Transformation Projects have been looking at leveraging
benefits targeting the lower levels of the automation scale introduced earlier on. Complexity of adding AI
capabilities to the Finance Function processes has dropped substantially which opens a perfect scenario to
enter in a new paradigm shift to start walking the path towards the Smart Finance Function.
David Santos Hernández
Founder & CEO at The Future of Finance Function
davidsantos@thefutureoffinancefunction.com